QuestionBelow is a chart that shows the amount of precipitation that falls each month (in inches) In a small Utah towm. Using thisdata, find the quadratic regression relating ralnfall (y) to month (x) with .x = 1 corresponding to January using elther acalculator or spreadsheet program.Rain Fall(inches)4.32MonthJanuaryFebruary4.06March3.30April1.25May0.46June0.21July0.05August0.10September0.15October1.40November2.65December2.75Select the correct answer below:O -0.128r2 – 1.84x + 6.74O 0.128x2 - 1.84x - 6.74O 0.128r2 – 1.84x + 6.74O 0.128x2 + 1.84x + 6.74

Given information: Month (x) Rainfall (y) January 4.32 February 4.06 March 3.30 April…

Answered: Question Below is a chart that shows…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A biology student is investigating the claim that the temperature can be predicted by counting cricket chirps. She has found a linear regression equation T=42.2+0.21r, where T is the temperature in degrees Fahrenheit and r is the number of chirps per minute. What is the predicted temperature for 90 chirps per minute? 18.9 degrees 227.6 degrees 23.3 degrees 61.1 degrees
Use the data shown in the graph to write a quadratic regression equation. Then predict the box office revenue in 2013. Let x represent the number of years since 2000. Millions of dollars 32- 189 31+ 30- Box office revenue 28.9 29.4 28.7 30.8 29- 28- 27- 2004 2005 2006 2007 2008 2009 Year
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
Listed below are the numbers of cricket chirps in 1 minute and the corresponding temperatures in °F. Find the regression equation, letting chirps in 1 minute be the independent (x) variable. Find the best predicted temperature at a time when a cricket chirps 3000 times in 1 minute, using the regression equation. What is wrong with this predicted temperature? Chirps in 1 min Temperature (°F) 913 1086 969 1081 1249 1138 1153 850 78.9 82.2 79.6 86.3 92 90.8 88 68.1 C The regression equation is y = + (x. (Round the y-intercept to one decimal place as needed. Round the slope to four decimal places as needed.)
Use the following information to answer the question. The following linear regression model can be used to predict ticket sales at a popular water park. Ticket sales per hour = -631.25 + 11.25(current temperature in °F) What is the predicted number of tickets sold per hour if the temperature is 86°F? Round to the nearest whole ticket. O About 336 tickets O About 252 tickets O About 276 tickets About 301 tickets
The percentage of adults who smoke in America is given below. Year America 1970 37.4 mon n 1980 33.2 1990 25.3 2000 23.1 2010 19.4 a) Write the linear regression model for A(x), representing the percentage of adult smokers in America x years since 1970. [Use your calculator OR Desmos to find the equation, Round coefficients to the nearest 100"] b) What is the slope of A(x)? Explain its meaning. c) Solve A(x) = 15. Show your work. Explain the meaning of your answer. d) Suppose in 2005, there were approximately 200 million adults in America. Use your model to estimate how many were smokers. Show how you get your answer.
need help with following
Please help me. Thankyou.
An automotive sales manager wishes to examine the relationship between age (years) and sales price ($) for used automobiles of a certain brand. The accompanying table contains data for a sample of a certain make and model that were listed for sale at a car shopping website. b. Fit a quadratic regression model to predict price and state the quadratic regression equation.

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